#' Mixture hidden Markov model for the mvad data
#'
#' A mixture hidden Markov model (MHMM) fitted for the \code{\link[TraMineR]{mvad}} data.
#'
#' @format A mixture hidden Markov model of class \code{mhmm}:
#' two clusters including 3 and 4 hidden states.
#' No covariates.
#'
#'
#' @details
#' The model is loaded by calling \code{data(mhmm_mvad)}. It was created with the
#' following code:
#' \preformatted{
#' data("mvad", package = "TraMineR")
#'
#' mvad_alphabet <-
#'   c("employment", "FE", "HE", "joblessness", "school", "training")
#' mvad_labels <- c("employment", "further education", "higher education",
#'   "joblessness", "school", "training")
#' mvad_scodes <- c("EM", "FE", "HE", "JL", "SC", "TR")
#' mvad_seq <- seqdef(mvad, 17:86, alphabet = mvad_alphabet,
#'   states = mvad_scodes, labels = mvad_labels, xtstep = 6)
#'
#' attr(mvad_seq, "cpal") <- colorpalette[[6]]
#'
#' # Starting values for the emission matrices
#' emiss_1 <- matrix(
#'   c(0.01, 0.01, 0.01, 0.01, 0.01, 0.95,
#'     0.95, 0.01, 0.01, 0.01, 0.01, 0.01,
#'     0.01, 0.01, 0.01, 0.95, 0.01, 0.01),
#'   nrow = 3, ncol = 6, byrow = TRUE)
#'
#' emiss_2 <- matrix(
#'   c(0.01, 0.01, 0.01, 0.06, 0.90, 0.01,
#'     0.01, 0.95, 0.01, 0.01, 0.01, 0.01,
#'     0.01, 0.01, 0.95, 0.01, 0.01, 0.01,
#'     0.95, 0.01, 0.01, 0.01, 0.01, 0.01),
#'   nrow = 4, ncol = 6, byrow = TRUE)
#'
#' # Starting values for the transition matrix
#'
#' trans_1 <-  matrix(
#'   c(0.95, 0.03, 0.02,
#'     0.01, 0.98, 0.01,
#'     0.01, 0.01, 0.98),
#'   nrow = 3, ncol = 3, byrow = TRUE)
#'
#' trans_2 <-  matrix(
#'   c(0.97, 0.01, 0.01, 0.01,
#'     0.01, 0.97, 0.01, 0.01,
#'     0.01, 0.01, 0.97, 0.01,
#'     0.01, 0.01, 0.01, 0.97),
#'   nrow = 4, ncol = 4, byrow = TRUE)
#'
#' # Starting values for initial state probabilities
#' initial_probs_1 <- c(0.5, 0.25, 0.25)
#' initial_probs_2 <- c(0.4, 0.4, 0.1, 0.1)
#'
#' # Building a hidden Markov model with starting values
#' init_mhmm_mvad <- build_mhmm(observations = mvad_seq,
#'   transition_probs = list(trans_1, trans_2),
#'   emission_probs = list(emiss_1, emiss_2),
#'   initial_probs = list(initial_probs_1, initial_probs_2))
#'
#' # Fit the model
#' set.seed(123)
#' mhmm_mvad <- fit_model(init_mhmm_mvad, control_em = list(restart = list(times = 25)))$model
#'
#' }
#'
#' @seealso Examples of building and fitting MHMMs in \code{\link{build_mhmm}} and
#' \code{\link{fit_model}}; and \code{\link[TraMineR]{mvad}} for more information on the data.
#'
#' @docType data
#' @keywords datasets
#' @name mhmm_mvad
#' @examples
#' data("mhmm_mvad")
#'
#' summary(mhmm_mvad)
#'
#' if (interactive()) {
#'   # Plotting the model for each cluster (change with Enter)
#'   plot(mhmm_mvad)
#' }
#'
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